#ifndef _LIBSVM_H
#define _LIBSVM_H

#define LIBSVM_VERSION 317

#ifdef __cplusplus
extern "C" {
#endif

    extern int libsvm_version;

    struct svm_node
    {
        int index;
        double value;
    };

    struct svm_problem
    {
        int l;
        double *y;
        struct svm_node **x;
    };

    enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR };	/* svm_type */
    enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */

    struct svm_parameter
    {
        int svm_type;
        int kernel_type;
        int degree;	/* for poly */
        double gamma;	/* for poly/rbf/sigmoid */
        double coef0;	/* for poly/sigmoid */

        /* these are for training only */
        double cache_size; /* in MB */
        double eps;	/* stopping criteria */
        double C;	/* for C_SVC, EPSILON_SVR and NU_SVR */
        int nr_weight;		/* for C_SVC */
        int *weight_label;	/* for C_SVC */
        double* weight;		/* for C_SVC */
        double nu;	/* for NU_SVC, ONE_CLASS, and NU_SVR */
        double p;	/* for EPSILON_SVR */
        int shrinking;	/* use the shrinking heuristics */
        int probability; /* do probability estimates */
    };

//
// svm_model
//
    struct svm_model
    {
        struct svm_parameter param;	/* parameter */
        int nr_class;		/* number of classes, = 2 in regression/one class svm */
        int l;			/* total #SV */
        struct svm_node **SV;		/* SVs (SV[l]) */
        double **sv_coef;	/* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
        double *rho;		/* constants in decision functions (rho[k*(k-1)/2]) */
        double *probA;		/* pariwise probability information */
        double *probB;
        int *sv_indices;        /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */

        /* for classification only */

        int *label;		/* label of each class (label[k]) */
        int *nSV;		/* number of SVs for each class (nSV[k]) */
        /* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
        /* XXX */
        int free_sv;		/* 1 if svm_model is created by svm_load_model*/
        /* 0 if svm_model is created by svm_train */
    };

    struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param);
    void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target);

    int svm_save_model(const char *model_file_name, const struct svm_model *model);
    struct svm_model *svm_load_model(const char *model_file_name);

    int svm_get_svm_type(const struct svm_model *model);
    int svm_get_nr_class(const struct svm_model *model);
    void svm_get_labels(const struct svm_model *model, int *label);
    void svm_get_sv_indices(const struct svm_model *model, int *sv_indices);
    int svm_get_nr_sv(const struct svm_model *model);
    double svm_get_svr_probability(const struct svm_model *model);

    double svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* dec_values);
    double svm_predict(const struct svm_model *model, const struct svm_node *x);
    double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates);

    void svm_free_model_content(struct svm_model *model_ptr);
    void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);
    void svm_destroy_param(struct svm_parameter *param);

    const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param);
    int svm_check_probability_model(const struct svm_model *model);

    void svm_set_print_string_function(void (*print_func)(const char *));

#ifdef __cplusplus
}
#endif

#endif /* _LIBSVM_H */
